Perfecting a Video Game with Game Metrics
Vol 19, No 3: June 2021

Author identification in bibliographic data using deep neural networks

Firdaus Firdaus (Universitas Sriwijaya)
Siti Nurmaini (Universitas Sriwijaya)
Reza Firsandaya Malik (Universitas Sriwijaya)
Annisa Darmawahyuni (Universitas Sriwijaya)
Muhammad Naufal Rachmatullah (Universitas Sriwijaya)
Andre Herviant Juliano (Universitas Sriwijaya)
Tio Artha Nugraha (Universitas Sriwijaya)
Varindo Ockta Keneddi Putra (Universitas Sriwijaya)

Article Info

Publish Date
01 Jun 2021


Author name disambiguation (AND) is a challenging task for scholars who mine bibliographic information for scientific knowledge. A constructive approach for resolving name ambiguity is to use computer algorithms to identify author names. Some algorithm-based disambiguation methods have been developed by computer and data scientists. Among them, supervised machine learning has been stated to produce decent to very accurate disambiguation results. This paper presents a combination of principal component analysis (PCA) as a feature reduction and deep neural networks (DNNs), as a supervised algorithm for classifying AND problems. The raw data is grouped into four classes, i.e., synonyms, homonyms, homonyms-synonyms, and non-homonyms-synonyms classification. We have taken into account several hyperparameters tuning, such as learning rate, batch size, number of the neuron and hidden units, and analyzed their impact on the accuracy of results. To the best of our knowledge, there are no previous studies with such a scheme. The proposed DNNs are validated with other ML techniques such as Naïve Bayes, random forest (RF), and support vector machine (SVM) to produce a good classifier. By exploring the result in all data, our proposed DNNs classifier has an outperformed other ML technique, with accuracy, precision, recall, and F1-score, which is 99.98%, 97.98%, 97.86%, and 99.99%, respectively. In the future, this approach can be easily extended to any dataset and any bibliographic records provider.

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Journal Info





Computer Science & IT


Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of ...